Are Workers’ Remittances Relevant for Credit Rating Agencies?
Rolando Avendaño
OECD Development Centre
Norbert Gaillard
Bank for International Settlements
Sebastián Nieto Parra
OECD Development Centre
Abstract
Remittances flows are an important source of financing for developing countries. In addition to the
microeconomic impact at the household level, remittances have grown into an important pillar of
macroeconomic stability, acting to reduce financial vulnerabilities, lessen probability of current account
reversals and ultimately reduce credit risk. This paper, assessing the country risk models for the three
main Credit Rating Agencies (CRAs), focuses on the role of workers’ remittances in the estimation of the
sovereign ratings of 83 emerging countries over four dimensions. First, it explores the role that workers’
remittances play in country risk models. Second, it assesses the extent to which CRAs take remittances
into account in determining ratings. Third, it seeks to capture the potential effect that remittances have
for sovereign ratings when considered in rating models. Fourth, it attempts to assign sovereign ratings to
unrated Latin American countries. We conclude that while CRAs take remittances flows nominally into
account as a determinant of ratings, remittances have a real and significant impact on a certain type of
sovereign rating, through the reduction of the volatility of external flows as well as the improvement of
the solvency ratio. Such effects are concentrated on a set of low and middle income countries, for which
the dependence on remittances is high.
JEL Classification: O11, F24, F3, G24
Keywords: credit rating agencies, remittances, sovereign ratings, emerging and developing capital
markets.
THIS DRAFT 09 MAY 20091
I. Introduction
1
The authors are indebted to Sara Bertin, Thomas Dickinson, Kiichiro Fukasaku, Juan de Laiglesia, Javier Santiso and the LEO
(Latin American Economic Outlook) team of the OECD Development Centre for helpful comments and discussions. The views
expressed in this paper do not necessarily reflect those of the OECD. Mailing address: OECD Development Centre, 2, rue André
Pascal, 75775 Paris Cedex 16, France.
1
Workers’ remittances can have an effect in the way that country risk is assessed. Previous research on the
access of sovereigns to international capital markets (see Ratha (2006) and World Bank (2006)) suggests
that sovereign creditworthiness could be improved by including remittances flows in key indebtedness
indicators, such as debt-to-exports and debt service to current account ratios. These have been identified
in the literature as common determinants of country ratings.
It is worth noting two surveys at the crossroads of the literature on sovereign ratings and remittances
flows. First, Ratha et al. (2007) define a standard ratings model and find that a number of unrated
countries would be likely to have higher ratings than expected, notably on account of foreign currency
inflows such as remittances. According to Ratha (2006), “country credit ratings by major international
rating agencies often fail to account for remittances”. Second, rating agencies note in their country
studies that remittances matter to determine ratings for countries in which this flow is considerable. Fitch
(2008) underlines that remittance flows may positively impact ratings (e.g. El Salvador)2. In its outlook for
Mexico, Standard and Poor’s (2005) stresses remittances’ importance as an income source for the
balance of payments, and their impact on other determinants of sovereign ratings, such as public
finances. More recently, Moody’s highlights that, for a country like the Philippines, a slower economic
growth for 2009 would also be explained by a decline in remittances, which account for more than 10% of
domestic output and are a major driver of consumption3.
Despite these stylized facts, little research has been devoted to analyse the impact that remittances have
on sovereign ratings assigned by CRAs. Our paper attempts to address this question by building a rating
model over a long time span (1993-2006), and estimating ratings for a sample of 83 emerging countries
and the three main CRAs. Concretely, this paper seeks to answer three key questions: Do rating agencies
really take remittances fully into account in their analyses? How can we capture the effect of remittances
on ratings? And finally, what is the potential effect of remittances when included in market variable
estimations?
With this purpose, it is crucial to understand why CRAs should take remittances into consideration when
assigning ratings. Although the effects of workers’ remittances at the macro and the micro level have
been largely studied (see World Bank, 2006 for a review of the literature), the evidence on the
implications for ratings is still scarce (World Bank 2006, Ratha 2007). The question is crucial given the
importance of remittances flows towards the developing world, with Latin America being no exception.
Over the last twenty years remittances have increased considerably, even by comparison to other capital
flows to developing economies. Currently, remittances account for around one third of total financial
flows to the developing world. With foreign direct investment (FDI), remittance flows to developing
countries overtook Official Development Assistance (ODA) in the nineties and for some developing
countries reached comparable and even higher levels than FDI flows. In that context, Ratha (2004) and
Ratha, Mohapatra and Xu (2008) provide an in-depth analysis of the importance of remittances since the
late 1990s.
In Latin American and Caribbean countries the increase in remittances flows is also considerable and their
proportion is high with respect to other external flows. During the period 1970-2006, remittances
accounted for about 50 percent of FDI and were twice as large as ODA flows. In 2006, remittances
2
3
See for instance Standard and Poor's (2005) and Fitch (2008).
“Moody's: Slowing remittances hurt RP”, Manila Bulletin, February 14, 2009 (online article).
represented more than 80 percent of FDI and more than eight times ODA flows. This is particularly clear
in some Central American countries.
During the same year, the volume of remittances towards developing countries reached about 70 percent
and 85 percent of FDI and ODA flows respectively.
Figure 1. Net flows to developing countries
300
250
USD Billions
200
150
100
50
-50
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
1978
1976
1974
1972
1970
1968
1966
1964
1962
1960
0
Year
ODA
FDI Inflows
Bonds
Equity
Remittances
Source: Authors, based on World Bank and OECD data, 2009.
At the macro-economic level, an important characteristic concerns remittances’ impact on the balance of
payments, in particular when compared with other capital flows. Figure 2 exhibits the volatility of the
major capital flows to emerging countries over the period 1990-2006.
Figure 2. Volatility of flows by period 1990-2006
Volatility of Inward Flows
0.018
1990-1999
0.016
2000-2007
0.014
0.012
0.01
0.008
0.006
0.004
0.002
0
Remittances
Portfolio
FDI
Source: Authors based on World Bank and OECD data
3
ODA
Exports
Note: the volatility of a quantity is defined as its coefficient of variation (the standard deviation of the quantity divided by the
mean of its absolute value). The normalisation avoids finding larger volatilities for larger flows. It is calculated for each recipient
and then averaged over all developing countries in the sample.
By comparing the volatility of remittances with respect to other flows we notice they have a lesser
volatility with respect to portfolio flows (equity and bond flows) but also with respect to FDI flows. Even
regarding ODA flows, remittances have a lower volatility than other capital flows. This effect is clearer in
the later years of the sample. Importantly, the correlation between remittances and other flows is small,
contributing to the stability of the total external flows. The correlation of remittances with FDI and ODA
are close to 0.20 and 0.0 respectively.
Migrants’ remittances constitute a large source of foreign capital in many Latin American countries and
can be considered as a stable source of financing compared with other financial flows. Remittances, in
the same way as foreign investment or exports, are important items in the balance of payments,
contributing to mitigate credit risk at the country level. More precisely, remittances strengthen financial
stability by reducing the probability of current account reversals (Bugamelli and Paterno, 2005). This, in
turn, can be related to the probability of default studied in country risk models. In the same way,
remittances can have a countercyclical effect in most countries of the region, thus significantly reducing
growth volatility (Fajnzylber and Lopez 2007). The overall conclusion of Close to Home, the
comprehensive World Bank study on Latin America, is that remittances are an engine for development,
but that they are neither “manna from heaven” nor a substitute for sound development policies.
Empirical evidence shows that remittances can have a positive impact on economic growth and poverty
reduction in developing countries by allowing capital accumulation at the household level (Giuliano and
Marta Ruiz-Arranz , 2005; Osili, 2006; Dustmann and Kirchamp (2001). For the case of Latin American
countries, we also find a positive effect of remittances at the microeconomic level (see Massey and
Parrado , 1998 for the case of Mexico and Cardona Sosa and Medina, 2006 for Colombia).
However, as pointed out elsewhere, migrant-based income can become costly to emerging countries
when resources are mismanaged. Remittances may reduce the government’s incentive to maintain fiscal
policy discipline (Chami et al., 2008). Moreover, this dependence raises a moral hazard problem by
reducing the political will to implement reforms and pushing real exchange appreciation. These findings
are consistent with Amuedo-Dorantes and Pozo (2004) who relate higher remittances flows to the
reduction of the receiving country’s competitiveness.
Remittances should matter when country risk is assessed in developing countries given the direct effect
that they have on the balance of payments (e.g., stability in the capital flows, exchange rates) and more
generally on the real economy (e.g., economic growth, output volatility, poverty reduction). Indeed,
remittances can play a positive role in the credit risk of a country and this impact can be observed directly
(e.g., stability of balance of payments) or indirectly (e.g. reduction of income inequality, creation of new
firms). For Latin American countries, the central empirical analysis of this paper, remittances are crucial,
given their high level with respect to the size of the economies (e.g., Guyana, Honduras, Haiti, Jamaica, El
Salvador) or given their high level in absolute values (e.g., Mexico, Brazil, Colombia, Guatemala, El
Salvador, Ecuador). Differences among countries in the region remain important. Figure 3 shows that
remittances are particularly relevant for Central American and Caribbean countries.
Figure 3. Remittances in Latin America – 2006
25%
25000
Workers Remittances
Chile
Venezuela, RB
Brazil
Argentina
Uruguay
Panama
Dominica
Peru
Colombia
Costa Rica
Mexico
Bolivia
Paraguay
0
Belize
0%
Ecuador
5000
Dominican …
5%
Guatemala
10000
Nicaragua
10%
Jamaica
15000
El Salvador
15%
Haiti
20000
Guyana
20%
Honduras
%
30000
Remittances/GDP
USD Millions
30%
Source: Authors calculation, based on Global Development Finance, 2009.
Workers’ remittances can therefore have both direct and indirect impacts on national economies. In this
paper we analyse the relationship between remittances and country credit risk through the balance of
payment channel. More precisely, we study the impact of remittances on country’s creditworthiness via
two dimensions. First, we analyse a common channel to measure remittances on country risk (Ratha
2005, World Bank 2006). By including remittances in a traditional solvency ratio studied in the ratings
literature (i.e., the ratio of debt to exports of goods and services), remittances can improve sovereign
ratings. Second, we introduce volatility of external flows as an additional variable explaining sovereign
ratings. This variable is related to the variability of trend of major inward external flows (FDI flows,
Portfolio flows, ODA, Bank loans, Exports and Remittances.). These flows are particularly important for
Latin America, where saving rates are low and dependence on external financing high. Our results show
that remittances can reduce volatility of external flows given their stability (when compared to other
flows) and low correlation with other external flows.
The remainder of this article is organised as follows. In section II, we provide a review of the literature on
sovereign ratings and in particular on the relevance of sovereign ratings for emerging economies as well
as the determinants of these ratings. Section III presents the most important stylised facts and analyses
the results of the econometric model. In particular, this section analyses the impact of remittances flows
on four representative models of the literature in ratings and our own model, where a counterfactual
analysis is presented. We also provide an empirical analysis for countries with a high share of remittances
(as a percentage of GDP). Finally, section IV provides concluding remarks and sketches the major policy
implications that follow from this research.
5
2. Review of the literature
In this section we provide first some empirical evidence on the relevance of CRAs for capital markets and
more especially for emerging economies. Second, we present the main results of the seminal papers
related to the determinants of ratings.
It has been pointed out that “the recent financial market turbulence has brought credit rating agencies
under fire” and academia as well as policy-makers argue for a reform of the business model of CRAs
(Portes 2008). Rating agencies are faced with a serious conflict of interest, to the extent that their
remuneration comes from rated issuers (for a theoretical analysis see Mathis, McAndrews and Rochet,
2008), both in the context of public of private borrowers. This is a crucial issue, given CRAs’ considerable
and increasing role on international capital markets. In this context, there is a large and useful literature
studying the impact of ratings on market prices and bond spreads. Focusing on market prices, Kaminsky
and Schmukler (2001) find that downgrades and upgrades have an impact on country risk and stock
returns: these rating changes are transmitted across countries, with neighbour-country effects being
more significant. They conclude that rating agencies may contribute to heighten financial instability.
The study of sovereign risk assessment has focused on comparing ratings to market spreads. For the
period 1987-1994, Cantor and Packer (1996) find a greater impact on spreads from a rating change in the
case of Moody’s or if it is related to speculative-grade countries. Reisen and Von Maltzan (1999) show
that, during the period 1989-1997, Fitch, Moody’s and S&P’s downgrades have a significant impact on
spreads, contrary to upgrades, which were anticipated by the market. For them, sovereign ratings have
the potential to moderate euphoria among investors on emerging markets but rating agencies failed to
exploit that potential in the 1990s. Sy (2001) highlights the strong negative relationship between ratings
and EMBI+ spreads declines during periods of high risk aversion (e.g., 1997-1998). Mora (2006) examines
Moody’s and S&P’s ratings and concludes that the procyclicality of ratings is not ascertained when
considering the post Asian crisis years. Analyzing sovereign ratings issued by the three agencies for 19932007, Gaillard (2009) finds that the procyclicality of ratings was much sharper during periods of high risk
aversion (1997-1998 in particular) than periods of low risk aversion (2005-2007). He also highlights the
greater stability of Moody’s ratings. In a different way, Cavallo et al (2008) develop a simple Hausman
specification test and find that there is some informational content in sovereign ratings that is not
completely captured by market spreads. Additional tests reinforce their conclusion that ratings matter.
Lastly, going beyond the traditional “ratings vs. spreads” view, Roubini and Manasse (2005) present an
original sovereign risk assessment methodology by using a binary recursive tree. This enables them to
better discuss appropriate policy options to prevent crises.
The literature focusing on sovereign ratings methodology has expanded since the mid 1990s. Cantor and
Packer (1996) identify five variables that may explain S&P and Moody’s sovereign ratings: per capita
income, inflation, external debt, level of economic development and default history. Jüttner and
McCarthy (2000) show that Cantor and Packer’s model becomes less accurate after the Asian crisis. They
suggest that the determinants of 1998 ratings are the current account balance, the indicators for
economic development and default history, the interest rate differential vis-à-vis the USD, and the range
of problematic assets. Nevertheless, several follow-up studies corroborate Cantor and Packer’s results.
For Afonso (2003), most significant variables for 2002 ratings (per capita income, inflation, indicators for
economic development and default history) are already determinants for Cantor and Packer. Moody’s
own study (Moody’s 2004) produces a similar finding: two of their four explanatory variables (per capita
GDP and external debt) are the same as Cantor and Packer’s. The main finding of Moody’s is the
incorporation of a political variable that significantly improves the model. For Rowland (2005), the level of
international reserves, as a share of GDP, and the openness of the economy are additional relevant
determinants. Sutton (2005)’s findings are consistent with previous papers. He also argues that the
maturity structure of structure of international banking claims against both private and public sector
entities in the country is a significant variable.
7
3. Empirical model
3.1.1. Data Description
As noted in the previous section, the literature on sovereign ratings is extensive. We have tried to focus
on the most representative work to identify the variables considered by agencies when assigning a rating
to public borrowers. The traditional approach in the literature has been to regress the dependent variable
(sovereign rating) on a series of macroeconomic and institutional indicators.
Table 1 summarizes the period and variables used by Cantor and Packer (1996), Rowland and Torres
(2004), Sutton (2005) and Mora (2006) to analyze the determinants of sovereign ratings. Whereas Cantor
and Packer and Sutton base their analysis on a cross-country study, Rowland and Torres and Mora use
panel data to estimate rating determinants. Most of these studies use one or more of the available
ratings published by the three main rating agencies, Standard and Poor’s, Moody’s and Fitch. Table 1
contains also our preferred model, that summarizes our analysis of previous rating models.
Table 1. Summary of models and variables
•
•
•
•
•
•
•
•
Spread (lag)
•
•
•
•
•
Dummy default on bonds and bak debt
•
•
Dummy European Union
•
•
•
Corrruption Index
•
•
•
Indicator of Economic Development
1993-2008
•
Default variable (diff. for each model)
2009
•
Default History
Our model
•
EMBI Dynamic
•
Volatility External Flows
•
Ratios short-term bank/Total claims
1986-2001
Openness ((Exports + Imports)/GDP)
2006
•
•
•
Current Account Balance/GDP
Mora
•
Reserves/GDP
•
Reserves
2004
Spread
1987-2001
2005
Debt Service/GDP
•
2004
Sutton
•
Total debt/ Exports
•
Rowland and Torres
•
External debt/exports
•
•
External Debt / Exports
Fiscal balance
•
1995
External Debt/GDP
Inflation
•
Period
1996
External balance
GDP growth
•
Year
Cantor and Packer
Fiscal Balance/GDP
Income per capita
Independent Variables
Sovereign Rating
Institutional rating
Dependent
Variable
•
•
•
•
Source: the authors based on Cantor and Packer (1996), Rowland and Torres (2004), Sutton (2005) and Mora (2006)
The results presented in the table above are straightforward. Sovereign ratings are associated to a
country’s fundamentals and, in contrast with sovereign spreads (e.g., Eichengreen and Mody, 2006),
endogenous factors are only analysed. More precisely, macroeconomic conditions (e.g., inflation rate,
GDP growth), solvency ratios (e.g., external debt over exports, external debt service over GDP) and
structural aspects (e.g., GDP per capita, economic development) are employed as determinants of
sovereign ratings.
In this paper we use data on annual ratings from the three main rating agencies: Standard and Poor’s,
Moody’s and Fitch. The covered period is 1993-2006, the frequency is annual and the initial sample
includes 83 rated countries (excluding High Income countries according to World Bank’s definition). Data
on ratings has been transformed linearly according to Table 2.
Table 2. Rating Linear Transformation
S&P's
Fitch
Moody's
Rating
AAA
AA+
AA
AAA+
A
ABBB+
BBB
BBBBB+
BB
BBB+
B
BCCC+
CCC
CCCCC
C
SD
AAA
AA+
AA
AAA+
A
ABBB+
BBB
BBBBB+
BB
BBB+
B
BCCC+
CCC
CCCCC
C
DDD
DD
D
Aaa
Aa1
Aa2
Aa3
A1
A2
A3
Baa1
Baa2
Baa3
Ba1
Ba2
Ba3
B1
B2
B3
Caa1
Caa2
Caa3
23
22
21
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0
D
Ca
C
Source: Author’s transformation, based on Rating agencies methodologies and Gaillard (2009)..
Data for macroeconomic variables comes from the World Development Indicators and data on national
debt from the Global Development Finance (World Bank). Table 3 provides a résumé of the main
macroeconomic variables used across the different rating models.
9
Table 3. Descriptive Statistics for Variables
Variables
GDP (current US$)
GDP (constant 2000 US$)
GDP growth (annual %)
GDP per capita (constant 2000 US$)
GDP per capita, PPP (current international $)
Inflation, consumer prices (annual %)
Inflation, GDP deflator (annual %)
Consumer price index (2000 = 100)
Real effective exchange rate index (2000 = 100)
Official exchange rate (LCU per US$, period average)
Changes in net reserves (BoP, current US$)
Current account balance (% of GDP)
Current account balance (BoP, current US$)
Exports of goods and services (BoP, current US$)
Exports of goods, services and income (BoP, current US$)
Foreign direct investment, net (BoP, current US$)
Foreign direct investment, net inflows (% of GDP)
Imports of goods and services (BoP, current US$)
Imports of goods, services and income (BoP, current US$)
Total reserves (% of external debt)
Total reserves (includes gold, current US$)
Total reserves in months of imports
Total reserves minus gold (current US$)
Workers' remittances, receipts (BoP, current US$)
Net capital account (BoP, current US$)
Bank capital to assets ratio (%)
Bank nonperfoming loans to total gross loans (%)
Risk premium on lending (%)
S&P/EMDB indexes (annual % change)
Export quantum/quantity index (2000 = 100)
Import value index (2000 = 100)
Gross National Product
Ratings Fitchs
Ratings Moodys
Ratings Standard and Poors
Fiscal Budget
Workers Remittances
Workers Remittances / GDP
Volatility of GDP Growth
Volatility of External flows (incl. remittances)
Volatility of External flows (excl. remittances)
Solvency Ratio (Debt / Exports)
Solvency Ratio (Debt / Exports) excl. remittances
Obs
Mean
Std. Dev.
1661
1655
1649
1655
1636
1475
1642
1497
772
1590
1441
1437
1441
1441
1441
1419
1463
1441
1441
1408
1545
1412
1545
1076
1073
391
422
562
425
1179
1284
1483
485
603
614
858
1076
1072
1378
1150
1150
1261
1340
7.13E+10
6.62E+10
3.777404
1904.606
4154.623
52.26535
86.95552
87.18809
4489.065
503.812
-1.85E+09
-3.532393
2.39E+08
1.95E+10
2.05E+10
1.50E+09
3.352745
1.89E+10
2.14E+10
46.78096
1.13E+10
3.935548
1.07E+10
1.03E+09
8.98E+07
10.40614
10.42891
8.088065
18.53672
94.06167
95.04032
6.84E+10
9.735547
9.972587
9.74107
-2.447704
1.03E+09
0.0369062
3.486176
0.0049457
0.0050462
240.9641
247.5379
2.01E+11
1.73E+11
6.394133
1673.767
3159.793
337.7307
642.4122
54.11652
121757.5
1759.692
1.26E+10
-7.884468
1.05E+10
5.54E+10
5.80E+10
5.16E+09
10.4849
4.85E+10
5.26E+10
125.9236
6.08E+10
3.113531
6.00E+10
2.37E+09
9.64E+08
3.903248
8.365045
14.34093
47.35008
51.80754
48.91408
1.81E+11
3.015993
3.324188
2.983933
4.318253
2.37E+09
0.0476944
4.01902
0.0309158
0.030922
321.0831
319.7211
Sources: Global Development Finance, World Development Indicators, International Financial Statistics, 2009.
3.1.2. Testing Previous Models for Sovereign Ratings: The Effect of Remittances
We start by testing the four representative models proposed in the literature, for the period 1993-2006.
We expect to identify the most relevant determinants for ratings in our sample. Regarding previous
studies, the sample includes a larger number of countries and a 14-year time span. We run regressions on
OLS and fixed-effect panel data, using the sovereign rating of the three rating agencies as the dependent
variable4.
Moreover, we are interested in analysing the impact that remittances could have on the behaviour of
rating agencies. As presented, remittances flows can be shock absorbers for the economy, and they play a
role in reducing the country’s vulnerability by providing resources that can have a positive impact on the
balance of payments and on the appreciation of the exchange rate5. More generally, remittances can
improve a country’s creditworthiness and thereby facilitate its access to international capital markets.
One significant and key solvency ratio used in the literature to explain the behaviour of sovereign ratings
is the debt-to-exports ratio. We start by including this variable in the standard rating models introducing
remittances in the ratio’s denominator to capture the entire effect of the current account incomes. At
this point we only introduce remittances through the solvency ratio as our second core variable (i.e.,
volatility of external flows) is not studied in the literature.
An increase in the remittances (as well as for the case of the exports of goods and services) can have a
positive effect on the current account and can induce appreciations on the real exchange rate. By the
same, these revenues in the balance of payments can serve as a cushion against external shocks and then
reduce the risk of default of the external debt. In fact, since we are interested in the country’s capacity to
pay the entire total external debt (private and public), it is reasonable to include remittances in this ratio,
to capture total incomes received by nationals in the balance of payments.
To quantify the impact of remittances on sovereign ratings, we test the standard models for ratings by
excluding/including the flow of remittances in the external debt to exports ratio. Data on exports comes
from the Global Development Finance (GDF) and data on workers´ remittances from the International
Financial Statistics (IFS)6. Figure 4 exhibits the evolution of our solvency ratio (debt over exports) for Latin
American and Caribbean countries, where the relative impact of remittances in indebtedness indicators
remains heterogeneous. In general, the effect of remittances is higher in Central American and Caribbean
4
OLS estimations are not reported and we can provide them under request.
However as noted beforehand, remittances may increase moral hazard problems for government public finances and reduce
the competitiveness for other sectors of the economy (i.e. Dutch disease effect).
6 Data on exports from the Global Development Finance also include total workers’ remittances registered in the Balance of
Payments. The GDF defines Exports of Goods, Services and Income (XGS) as the total value of goods and services exported, and
receipts of compensation of employees, and investment income. In order to calculate our solvency ratio we first exclude
workers’ remittances and compensation of employees from the XGS variable (solvency ratio without remittances) and then we
include workers’ remittances (from the IFS) in the denominator of the solvency ratio (solvency ratio with remittances). Workers’
remittances, a transfer and not an income entry in the balance of payments, are treated as compensation of employees in Global
Development Finance because they are often uneasy to distinguish from compensation of non-resident workers and migrants.
We therefore have usually workers’ remittances and compensation of employees contained in the Export series. Workers’
remittances and compensation of employees comprise current transfers by migrant workers, wages and salaries earned by nonresident workers. In addition, migrants’ transfers, a part of capital transfers, are treated as workers’ remittances in Global
Development Finance. We therefore restrict our analysis to the series of “workers remittances”, and exclude compensation of
employees and migrants’ tranfsfers (as estimated by GDF database). See GDP Volume 1 for more details.
5
11
countries (e.g., El Salvador, Jamaica, Guatemala, Dominican Republic) than in other countries of the
region (e.g., Argentina, Brazil, Chile, Mexico, Peru, Venezuela).
Figure 4. Ratio of External debt over Exports (tdoverx_wr) and over Exports and Remittances (tdoverx)
Latin American and Caribbean countries
Argentina
Belize
800
Bolivia
250
450
700
400
200
600
500
350
300
150
250
400
200
100
300
150
200
50
100
100
50
tdoverx
tdoverx_wr
tdoverx
tdoverx
tdoverx_wr
Bolivia
250
600
200
500
400
150
300
100
200
50
100
tdoverx
tdoverx_wr
tdoverx
Costa Rica
tdoverx
2006
2005
2004
2003
2002
2001
2000
2006
2005
2004
2003
2002
2001
2000
2006
2005
2004
2003
2002
2001
2000
tdoverx
tdoverx_wr
El Salvador
Ecuador
tdoverx_wr
Grenada
250
350
1999
1993
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
tdoverx
1998
0
tdoverx_wr
400
1999
20
0
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
0
40
1997
50
1996
20
60
1995
100
1999
80
200
1995
1998
100
250
150
1997
Dominican Republic
100
40
tdoverx_wr
120
300
60
1996
tdoverx
120
80
1995
tdoverx_wr
Dominica
140
1994
1993
2006
2005
2004
2003
2002
1993
tdoverx_wr
2001
0
2006
2005
2004
2003
2002
50
1994
tdoverx
2001
2000
1999
1998
1997
1996
1995
0
2000
50
100
1999
100
1998
150
150
1997
200
1996
250
1994
1997
Colombia
200
1995
300
tdoverx_wr
250
1994
350
1993
1996
tdoverx
200
180
160
140
120
100
80
60
40
20
0
400
1994
1995
tdoverx_wr
Chile
Brazil
450
1993
1994
1993
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
2006
2005
2004
2003
2002
0
1998
tdoverx
2001
2000
1999
1998
1997
1996
1995
1994
1993
0
350
300
200
300
250
150
250
200
200
150
100
150
100
100
50
50
50
0
tdoverx
tdoverx_wr
tdoverx
tdoverx_wr
Source: Global Development Finance and International Financial Statistics, 2009.
tdoverx
tdoverx_wr
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
0
1993
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
0
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
tdoverx_wr
Belize
Argentina
500
450
400
350
300
250
200
150
100
50
0
1991
1990
1987
1989
0
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
0
1988
0
Figure 4. Ratio of External debt over Exports (tdoverx_wr) and over Exports and Remittances (tdoverx)
Latin American and Caribbean countries (cont.)
Guatemala
Haiti
Guyana
3500
350
3000
120
300
100
250
80
200
60
150
40
100
20
50
500
0
0
0
2500
2000
1500
140
tdoverx_wr
tdoverx_wr
tdoverx
2006
2005
2004
2003
2002
2001
120
3000
100
2500
2000
100
1500
2006
2005
2004
Panama
3500
150
tdoverx_wr
tdoverx
Nicaragua
200
2003
1993
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
0
Mexico
250
2000
50
1993
2006
2005
2004
2003
2002
2000
1998
1997
1996
1994
2001
0
1999
0
1995
20
1999
100
40
50
2002
100
1998
150
60
2001
80
2000
150
200
1999
100
1998
200
1997
120
1996
250
1997
250
160
300
1996
Latin America and Caribbean
300
1995
180
1994
200
tdoverx_wr
tdoverx
Jamaica
Honduras
350
1993
1995
tdoverx_wr
tdoverx
400
tdoverx
1994
1993
2006
2005
2004
2003
2002
2001
2000
1999
1997
1996
1995
1994
tdoverx_wr
tdoverx
1998
1000
1993
2005
2004
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
2006
400
140
2003
160
80
60
2004
2005
2006
2004
2005
2002
2001
2000
1999
1998
1997
1996
1995
1994
2006
2005
2004
2003
2002
2003
100
2003
120
tdoverx_wr
tdoverx
Peru
140
2002
Paraguay
2001
2000
1999
tdoverx_wr
tdoverx
tdoverx_wr
tdoverx
1998
1997
1996
1995
1994
0
1993
20
0
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
0
500
1993
40
1000
50
Uruguay
600
400
500
350
300
400
250
80
60
40
300
200
200
150
20
100
0
0
100
tdoverx_wr
tdoverx
tdoverx_wr
tdoverx
Venezuela
250
200
150
100
50
tdoverx
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
0
tdoverx_wr
Source: Global Development Finance and International Financial Statistics, 2009.
13
tdoverx
tdoverx_wr
2006
2001
2000
1999
1998
1997
1996
1995
1994
0
1993
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
50
Following the literature review, we opt for testing our hypothesis on a group of models on sovereign
ratings. Table 4 summarizes results of four representative models (Cantor and Packer (1996), Rowland
and Torres (2004), Sutton (2005) and Mora (2006)), for the three main agencies over the period 19932006, using both ratios, total debt over exports of goods and services, and workers’ remittances ( TDX )
and total debt over exports of goods and services ( TDX _ wr ).
Table 4a. Determinants of Sovereign Ratings (1993-2006)
Cantor and Packer (1996), Rowland and Torres (2004)
S&P
gdp_per_cap_ppp_curr
gdp_growth_annual
inflat_annual
fisc_budget_our
current_account_bal_%gdp
tdoverx
default_20
Cantor and Packer
Moodys
(iii)
(iv)
(i)
(ii)
With
Remittances
Without
Remittances
With
Remittances
0.0011***
[12.59]
-0.0729***
[3.61]
-0.0012*
[1.92]
0.0184
[0.66]
-0.1543***
[8.75]
-0.0129***
[7.58]
-0.8783**
[2.27]
0.0011***
[12.44]
-0.0720***
[3.55]
-0.0012*
[1.88]
0.0193
[0.69]
-0.1535***
[8.64]
-0.0116***
[7.18]
-0.7872**
[2.03]
Fitch
S&P
Rowland and Torres
Moodys
(ix)
(x)
(v)
(vi)
(vii)
(viii)
Without
Remittances
With
Remittances
Without
Remittances
With
Remittances
Without
Remittances
With
Remittances
0.0012***
[15.38]
-0.0516***
[3.20]
-0.0003
[0.91]
-0.0427*
[1.81]
-0.1109***
[7.77]
-0.0088***
[6.25]
-0.3678
[1.14]
0.0012***
[15.23]
-0.0507***
[3.13]
-0.0004
[0.93]
-0.0415*
[1.75]
-0.1105***
[7.69]
-0.0076***
[5.86]
-0.3079
[0.95]
0.0011***
[10.58]
-0.0467**
[2.27]
-0.001
[1.25]
-0.1157***
[3.22]
-0.0774***
[4.53]
-0.0071***
[3.66]
0.0096
[0.02]
0.0011***
[10.58]
-0.0468**
[2.28]
-0.001
[1.23]
-0.1145***
[3.19]
-0.0783***
[4.58]
-0.0065***
[3.56]
0.0755
[0.15]
-0.0086
[0.40]
-0.0012
[1.60]
-0.0086
[0.40]
-0.0012
[1.60]
0.0089***
[3.20]
-2.3087***
[5.32]
-0.0581***
[7.68]
0.0491*
[1.86]
0.0375**
[2.38]
0.0025
[0.31]
417
46
0.57
418
46
0.56
334
47
0.45
335
47
0.45
497
55
0.27
tdovergnp
tdtdsovergnp
res_gnp
openness
Fitch
(xi)
(xii)
Without
Remittances
With
Remittances
Without
Remittances
-0.0023
[0.12]
-0.0010**
[1.98]
-0.003
[0.15]
-0.0010*
[1.96]
-0.0466**
[2.30]
-0.0003
[0.41]
-0.0478**
[2.36]
-0.0003
[0.44]
0.0093***
[3.62]
-2.3789***
[5.50]
-0.0602***
[7.99]
0.0479*
[1.82]
0.0385**
[2.45]
0.0035
[0.45]
0.0070***
[2.66]
-1.3330***
[3.19]
-0.0385***
[5.70]
0.0374
[1.56]
0.0538***
[3.77]
-0.0047
[0.66]
0.0066***
[2.75]
-1.3697***
[3.29]
-0.0391***
[5.75]
0.0391
[1.64]
0.0547***
[3.84]
-0.0045
[0.64]
0.0180***
[6.11]
-0.3488
[0.67]
-0.0734***
[9.70]
0.0384
[1.45]
0.0596***
[3.05]
0.0178**
[2.19]
0.0164***
[6.08]
-0.5101
[0.97]
-0.0727***
[9.68]
0.0435
[1.64]
0.0614***
[3.14]
0.0170**
[2.10]
497
55
0.27
477
49
0.22
477
49
0.23
363
50
0.33
363
50
0.33
log_res
log_bank_res_to_bank_assets
log_tdtdsoverx
log_tdovergnp
Observations
427
428
Number of country_id
49
49
R-squared
0.53
0.52
Absolute value of t statistics in brackets
* significant at 10%; ** significant at 5%; *** significant at 1%
Table 4b. Determinants of Sovereign Ratings (1993-2006)
Sutton (2005)
Sutton
Moodys
S&P
Fitch
(xiii)
(xiv)
(xv)
(xvi)
(xvii)
(xviii)
With
Remittances
Without
Remittances
With
Remittances
Without
Remittances
With
Remittances
Without
Remittances
-2.2285***
[5.64]
-2.2598***
[5.73]
-1.2746***
[3.43]
-1.3135***
[3.53]
-0.3887
[0.81]
-0.4226
[0.88]
1.9091***
[9.06]
-0.9255***
[4.65]
0.7199***
[3.45]
-1.7545***
[6.34]
361
49
0.42
1.9046***
[9.03]
-0.9210***
[4.61]
0.7167***
[3.42]
-1.7517***
[6.33]
361
49
0.42
gdp_per_cap_ppp_curr
gdp_growth_annual
inflat_annual
fisc_budget_our
current_account_bal_%gdp
tdoverx
default_20
tdovergnp
tdtdsovergnp
res_gnp
openness
log_res
1.8452***
1.8383***
1.6582***
1.6515***
[9.72]
[9.69]
[10.46]
[10.39]
log_bank_res_to_bank_assets
-0.8874***
-0.8789***
-0.6375***
-0.6345***
[5.33]
[5.27]
[4.48]
[4.44]
log_tdtdsoverx
0.8156***
0.8232***
0.9460***
0.9063***
[4.07]
[4.13]
[5.23]
[4.98]
log_tdovergnp
-1.7675***
-1.7797***
-1.3803***
-1.3590***
[6.43]
[6.46]
[5.74]
[5.63]
Observations
488
488
479
479
Number of country_id
55
55
50
50
R-squared
0.4
0.4
0.38
0.37
Absolute value of t statistics in brackets
Absolute value of t statistics in brackets
* significant at 10%; ** significant *atsignificant
5%; *** significant
at 10%; **atsignificant
1%
at 5%; *** significant at 1%
15
Table 4c. Determinants of Sovereign Ratings (1993-2006)
Mora (2006)
Mora (level variable)
Moodys
Fitch
(i)
(ii)
(iii)
(iv)
(v)
(vi)
With
Without
With
Without
With
Without
Remittances Remittances Remittances Remittances Remittances Remittances
gdp_per_cap_ppp_curr
0.0012***
0.0012***
0.0014***
0.0015***
0.0010***
0.0010***
[10.34]
[10.37]
[14.06]
[14.18]
[6.52]
[6.50]
gdp_growth_annual
-0.0877***
-0.0871***
-0.0741***
-0.0746***
-0.0574**
-0.0587**
[4.57]
[4.55]
[4.40]
[4.46]
[2.54]
[2.60]
inflat_annual
-0.0012
-0.001
-0.0005
-0.0005
-0.0034
-0.0039
[0.18]
[0.15]
[0.62]
[0.62]
[0.32]
[0.37]
fisc_budget_our
0.0353
0.0358
-0.0645**
-0.0653**
-0.0141
-0.0158
[1.22]
[1.24]
[2.54]
[2.58]
[0.35]
[0.39]
current_account_bal_%gdp
-0.0931***
-0.0929***
-0.0788***
-0.0796***
-0.0708***
-0.0713***
[4.96]
[4.95]
[4.67]
[4.74]
[3.02]
[3.03]
tdoverx
-0.0041*
-0.0039*
-0.0041**
-0.0045**
0.0036
0.003
[1.87]
[1.87]
[2.07]
[2.40]
[1.42]
[1.22]
default_20
-0.4623
-0.4338
-0.135
-0.1095
0.4407
0.4112
[0.93]
[0.87]
[0.31]
[0.26]
[0.73]
[0.68]
spread
-0.0010***
-0.0010***
-0.0003***
-0.0003***
-0.0011***
-0.0011***
[8.38]
[8.41]
[3.30]
[3.19]
[7.62]
[7.54]
Observations
225
226
229
230
194
195
Number of country_id
27
28
27
28
25
26
R-squared
0.76
0.76
0.72
0.72
0.69
0.69
Absolute value of t statistics in brackets
* significant at 10%; ** significant at 5%; *** significant at 1%
S&P
Mora (lagged variable)
Moodys
Fitch
(vii)
(viii)
(ix)
(x)
(xi)
(xii)
With
Without
With
Without
With
Without
Remittances Remittances Remittances Remittances Remittances Remittances
0.0001
0.0002
0.0002***
0.0011
-0.0022
0.0001
[1.18]
[1.22]
[2.99]
[0.65]
[4.68]
[2.43]
0.1089***
0.1098***
0.0001
0.0680***
0.0001
-0.3482
[5.11]
[5.17]
[1.97]
[3.01]
[0.76]
[4.72]
-0.0082
-0.0079
0.0011
0.0001
0.0008
0.0859***
[1.10]
[1.06]
[0.07]
[2.15]
[0.52]
[1.89]
0.0199
0.0206
-0.0033*
0.0541
0.0776**
0.0011
[0.62]
[0.64]
[1.44]
[0.15]
[0.10]
[0.94]
0.0549***
0.0553***
0.0206
0.0207
0.0273
0.0275
[2.64]
[2.66]
[1.44]
[1.45]
[1.44]
[1.45]
-0.0041*
-0.0037
0.0462**
0.0463**
-0.3658
-0.0019
[1.67]
[1.60]
[4.75]
[1.43]
[1.06]
[0.13]
0.0814
0.1105
0.0677***
0.0002***
0.0852***
0.0784**
[0.15]
[0.20]
[0.60]
[4.78]
[2.40]
[0.52]
0.0005***
0.0004***
0.0263
-0.0032**
0.0002*
0.0002*
[3.29]
[3.26]
[2.15]
[2.01]
[1.94]
[0.72]
225
226
229
230
194
195
27
28
27
28
25
26
0.35
0.35
0.33
0.33
0.37
0.37
S&P
Results in Table 1 show that for most models the ratio debt over exports (with or without remittances) is
negative and significant for the three major rating agencies. Indeed, it is a key and relevant variable
explaining sovereign ratings. For instance, taking Cantor and Packer (1996) model, columns 1 to 6 in Table
4a show that the solvency ratio (i.e. Debt over Exports) is statistically significant at 1 per cent and
negatively correlated with sovereign ratings.
In addition to this ratio, other variables are crucial to explain ratings assigned by agencies: GDP per
capita, GDP growth, inflation rate, current account over GDP, historical default and international reserves
over GNP. Finally, when comparing the impact of including and excluding remittances on the ratio debt
over exports, the value of the coefficient is almost the same for both cases (in absolute terms), for all
rating models studied.
Results suggest that rating agencies are not sensitive to workers’ remittances when they rate sovereigns.
Indeed, an inclusion of remittances supposes a reduction of the solvency ratio and consequently a higher
coefficient (in absolute value) can compensate for the “remittances effect” in the sovereign rating. This
finding is explained empirically in detail for our general model.
3.1.3. Proposing a General Model and Testing Effect from Remittances
Traditional models on the determinants of ratings include a solvency indicator, such as the ratio debt to
exports. By introducing remittances (as suggested by Ratha, 2005), we have tested if remittances flows
play a role in reducing external vulnerabilities. In addition, we introduce a consistent explanatory variable
for sovereign ratings in which remittances can play a crucial role: the volatility of external flows.
As specified above, when compared to other external flows (i.e. exports, portfolio flows, FDI flows, ODA),
remittances display a much lower volatility and lower correlation to these flows; they can act as a cushion
vis-à-vis capital flights. We assess the volatility of external flows as a second channel through which
remittances flows are likely to affect sovereign ratings. Our hypothesis is that remittances can reduce the
total volatility of inward external flows, which is itself a powerful explanatory variable for sovereign
ratings.
We use the variance as a measure of volatility of external flows. We decompose the variance of inward
external flows as follows:
N
N
i 1
i j
Var external _ flows  ,t   wi2,t  Var ( X i ,t )  2 wi ,t w j ,t Cov( X i ,t , X j ,t )
where Var (external _ flows) ,t corresponds to the variance of inward external flows of country  at
time t, wi ,t is the weight of the external flow i with respect to the total external flows in country  ,
Var ( X i ,t ) is the variance of the external flow i as a share of GDP between t-5 and t, Cov ( X i ,t , X j ,t ) is
the covariance between the external flows over GDP i and j and from t-5 to t.
Figure 5 presents the volatility of external flows by including and excluding remittances. There is a
considerable reduction of the external flows volatility for some Central American countries (e.g.,
Honduras, Guatemala, Nicaragua) and high-remittance receptors (e.g. Ecuador, Colombia, El Salvador).
17
Figure 5a. Volatility of Inward External Flows with and without Workers’ Remittances
Bolivia
Argentina
Brazil
0.0035
0.0045
Volat External Flows
0.004
Volat External Flows
0.00035
0.0025
0.003
0.002
0.002
0.0015
0.0015
Volat External Flows (without remitt.)
0.0003
Volat External Flows (without remitt.)
0.0025
Volat External Flows
0.0004
0.003
Volat External Flows (without remitt.)
0.0035
0.00045
0.00025
0.0002
0.00015
0.001
0.001
0.0001
0.0005
0.00005
0
Ecuador
Dominican Repubic
0.0025
0.002
0.0014
Volat External Flows (without remitt.)
0.0012
0.0015
0.0008
0.001
0.0006
Peru
0.0012
Volat External Flows (without remitt.)
2005
2007
2004
2005
2006
2003
2004
2002
2001
2000
1999
2003
2007
2006
2002
2001
2007
2006
2005
2004
2003
2002
2001
2000
1999
2007
2006
2005
2004
2003
2002
2001
2000
1999
1997
1996
1995
1994
2007
2006
2005
2004
2003
2002
2001
2000
1997
1996
1995
1994
1993
2007
2006
2005
2004
2003
2002
2001
Rating
Rating
Venezuela
0.014
Volat External Flows
0.012
Volat External Flows (without remitt.)
0.01
0.008
0.0015
0.0006
1993
2007
2006
2005
2004
2003
2002
2001
2000
1999
2000
1999
1998
Volat External Flows (without remitt.)
0.002
0.0008
Volat External Flows (without remitt.)
Rating
0.0025
0.001
Volat External Flows
0.016
Volat External Flows
Volat External Flows
1998
1997
1996
1995
1994
1993
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
Uruguay
0.003
0.0014
Paraguay
Rating
0.0016
1993
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
0
1997
0.01
0.005
Volat External Flows (without remitt.)
1996
Rating
0.015
Volat External Flows (without remitt.)
0.009
0.008
0.007
0.006
0.005
0.004
0.003
0.002
0.001
0
Volat External Flows
1995
Volat External Flows (without remitt.)
Panama
1994
Volat External Flows
1994
2007
2002
1993
0
2006
0.001
0
2005
0.002
0.001
2004
0.003
0.002
2003
0.004
0.003
2001
0.004
2000
0.005
1999
0.006
0.005
1998
0.006
1997
0.007
0.1
0.09
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
Volat External Flows
0.007
Volat External Flows (without remitt.)
1996
1995
1994
1993
Mexico
Honduras
Volat External Flows
Volat External Flows
0.02
1993
1998
1997
1996
1995
1994
1993
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
0
Guatemala
Nicaragua
2000
0.0002
0
0.025
1999
Volat External Flows (without remitt.)
0.0004
0.0005
0.0009
0.0008
0.0007
0.0006
0.0005
0.0004
0.0003
0.0002
0.0001
0
1998
1997
1996
Volat External Flows
0.001
1999
Volat External Flows (without remitt.)
El Salvador
0.0016
Volat External Flows
1998
Volat External Flows
1998
1993
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1994
1993
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1995
0
0
1998
0.0002
1997
0.00005
1996
0.0001
0.0004
1995
0.00015
0.0006
1994
0.001
0.01
0.009
0.008
0.007
0.006
0.005
0.004
0.003
0.002
0.001
0
1995
1993
2007
2006
2005
2004
2003
2002
2001
2000
0.0002
0.0008
Volat External Flows
0.0045
0.004
0.0035
0.003
0.0025
0.002
0.0015
0.001
0.0005
0
Volat External Flows (without remitt.)
Volat External Flows (without remitt.)
0.0012
Costa Rica
Volat External Flows
0.00025
Volat External Flows
0.0014
1999
Colombia
Chile
0.0016
1998
1997
1996
1995
1994
1993
0
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1993
1994
0
1994
0.0005
0.006
0.001
Source: Global Development Finance and International Financial Statistics, 2009.
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
0
1995
0.002
0
1994
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
0
0.0005
1993
0.0002
1993
0.004
0.0004
Figure 5b. Average Volatility of External Flows with and without Workers’ Remittances
0.007
Impact of Remittances on Volatility of External Flows
0.006
Average 1992-2006
0.005
Average Volat External Flows
0.004
Volat External Flows (without remitt.)
0.003
0.002
0.001
Colombia
Brazil
Guatemala
El Salvador
Peru
Bolivia
Chile
Ecuador
Uruguay
Mexico
Argentina
Costa Rica
Honduras
Dominican Republic
Venezuela, RB
Paraguay
Nicaragua
0
Source: Authors’ calculation, based on Global Development Finance and International Financial Statistics, 2009.
Considering the results from the four standard models and our new volatility indicator presented above,
we propose the following model for our analysis (we name it General Model):
Rating i ,t   0   1GDP _ pc   2GDP _ growthi ,t   3 Inflat i ,t   4 Fisc _ budg i ,t   5CAi ,t   6TDX i ,t
  7 Default i ,t   8 Re servesi ,t   9Volat _ indicatori ,t   7 EMBI i ,t   t  i   i ,t
where Rating i ,t corresponds to the transformed rating of country i at time t, GDP _ pc is the per capita
GDP in current international dollars, GDP _ growth is the product’s growth, Inflat corresponds to
annual inflation, Fisc _ budg is the annual balance budget as a share of GDP, CA is the current account
position (as a share of GDP), TDX is the ratio of total debt to exports, Default is a dummy variable for
countries taking value 1 for countries having experienced a default during the previous 20 years,
Re serves is the ratio of reserves to GNP, Volat _ indicator is the volatility of external of flows,
EMBI is a dummy variable for those countries covered by the Bond Index calculated by JPMorgan,  t is a
year fixed effect,
 i is the country-individual effect and  i,t is an error term. Within this
setup,  6 measures the elasticity of sovereign ratings with respect to the ratio debt-to-exports, after
controlling for all the other factors (GDP, inflation, volatility, etc.). The term  t is capturing differences in
sovereign rating across time not explained by the other determinants.
19
In this model, we are mainly interested in the variables that can be affected by the flow of remittances,
particularly the volatility indicator (i.e., the volatility of inward external flows) and the solvency ratio (i.e.,
debt-to-exports). 7
Table 5 shows the results for our general model and the three rating agencies. We report results using
the two variables of interest, the volatility indicator and the solvency ratio, including and excluding
remittances8.
Table 5. General Model – All Sample
S&P
GDP per capital (PPP)
GDP growth (annual)
Annual inflation
Fiscal Budget
Current Account (% GDP)
Solvency Ratio (debt/exports)
Default dummy (20 years)
Reserves Ratio
Volatility External Flows
EMBI dummy
Observations
Number of country_id
R-squared
Standard errors in brackets
*** p<0.01, ** p<0.05, * p<0.1
(i)
With
remittances
0.0010***
[0.0001]
-0.0249
[0.0219]
-0.0013**
[0.0006]
-0.0204
[0.0332]
-0.1330***
[0.0208]
-0.0121***
[0.0018]
-1.2455***
[0.4294]
0.0091
[0.0167]
-155.7120***
[37.6048]
0.5923**
[0.2979]
360
43
0.528
Moodys
ME
4.1235
-0.0944
-0.0632
0.0498
0.4714
-2.6658
-0.9099
0.1401
-0.7552
0.1213
(ii)
Without
Remittances
0.0010***
[0.0001]
-0.03
[0.0202]
-0.0012**
[0.0006]
-0.0149
[0.0290]
-0.1375***
[0.0178]
-0.0111***
[0.0016]
-1.1585***
[0.4079]
0.0058
[0.0144]
-131.2598***
[28.3887]
0.6037**
[0.2595]
398
47
0.535
ME
4.1235
-0.1138
-0.0584
0.0364
0.4873
-2.4455
-0.8464
0.0893
-0.6366
0.1237
(iii)
With
remittances
0.0012***
[0.0001]
-0.0540***
[0.0177]
-0.0003
[0.0004]
-0.0743***
[0.0282]
-0.1031***
[0.0176]
-0.0084***
[0.0015]
-0.4639
[0.3641]
0.0335**
[0.0139]
-7.4536*
[3.8138]
0.0683
[0.2509]
353
39
0.571
ME
4.9482
-0.2048
-0.0146
0.1815
0.3654
-1.8506
-0.3389
0.5156
-0.0361
0.0140
Fitch
(iv)
Without
Remittances
0.0011***
[0.0001]
-0.0536***
[0.0170]
-0.0003
[0.0004]
-0.0453*
[0.0257]
-0.1140***
[0.0154]
-0.0072***
[0.0014]
-0.3248
[0.3577]
0.0249**
[0.0127]
-8.7014**
[3.7647]
0.0673
[0.2442]
390
44
0.556
ME
4.5359
-0.2032
-0.0146
0.1107
0.4040
-1.5863
-0.2373
0.3832
-0.0422
0.0138
(v)
With
remittances
0.0010***
[0.0001]
-0.0126
[0.0229]
-0.0011
[0.0008]
-0.1141**
[0.0495]
-0.0840***
[0.0187]
-0.0056**
[0.0022]
-0.2517
[0.6137]
-0.0176
[0.0270]
-173.4265***
[42.6915]
0.4242
[0.4211]
273
41
0.455
ME
4.1235
-0.0478
-0.0535
0.2788
0.2977
-1.2338
-0.1839
-0.2709
-0.8411
0.0869
(vi)
Without
Remittances
0.0010***
[0.0001]
-0.0119
[0.0215]
-0.001
[0.0008]
-0.1288***
[0.0424]
-0.0842***
[0.0178]
-0.0053***
[0.0020]
-0.3075
[0.5871]
-0.0214
[0.0234]
-115.0774***
[32.8760]
0.3523
[0.3655]
305
45
0.439
Source: Authors’ calculation.
First, we analyze results for regressions including remittances in the volatility of capital flows as well as in
the debt ratio. We focus on regressions (i), (iii) and (v) for Table 5. Not surprisingly, GDP per capita is
positive and significant at the 1 per cent level. GDP growth, on the contrary, is not significant for our
sample and it is negatively correlated with ratings (the exception being for Moody’s). An increase of
7
As for the case of the models presented above, we build an artificial ratio by subtracting the total amount of Workers’
Remittances from the variable Total Exports, and we name it TDX _ wr , this is, the ratio debt-to-exports excluding workers’
remittances. Again, when comparing the coefficient for the variable Total Debt/Exports with and without remittances, these are
very similar. A coefficient test shows that they are not significantly different from the previous regression. 7 By the same, in order
to analyse the impact of remittances through the volatility of capital flows, we subtract Workers’ Remittances to the calculation
of the volatility of external flows, and we name it Volat _ indicator _ wr , this is, the volatility of external flows by excluding
workers’ remittances.
8
A complementary approach consisted of defining a variable taking the difference between the two ratios and volatilities, this is:
 Debt  

Debt


1  
 Exports  Exports remit tan ces 
and
 2  Volatwith _ remit  Volatwith out_ remit ,
and test for the significance of these variables in the
general model. For the first difference, when including them in the model together with the ratio debt over exports it was
significant at 1%, but it becomes non-significant when excluding the ratio. The second difference is not significant in the model at
the 5% level (except for Fitch).
ME
4.1235
-0.0451
-0.0486
0.3147
0.2984
-1.1677
-0.2247
-0.3294
-0.5581
0.0722
inflation supposes a decrease in the rating, and this result is significant for Standard and Poor’s. The
balance budget is negative and significant for the case of Fitch. Unexpectedly, the current account
balance is negatively associated to the ratings, indicating that countries running current account deficits
may be better rated; there are multiple explanations for this result, one of them being that an important
number of commodity exporters, with fragile macroeconomic performance in the past, experienced
significant surpluses. Both variables of interest, the debt-to-exports ratio and the volatility of external
flows, are consistently negative and significant for all rating agencies. Indeed, additionally to the standard
variable used to explain the impact of remittances on ratings (i.e., the solvency ratio), the new variable
introduced (i.e., the volatility indicator) is an important one when explaining ratings. The variable default
is negatively correlated to the sovereign rating, as expected, and is significant for Standard and Poor’s.
The reserves-to-national product ratio is positively related to the rating in the case of S&P and Moody’s,
highlighting the increasing role of precautionary reserves for impeding defaults.
Now we focus on regressions (ii), (iv) and (vi), that considers the new variables excluding remittances
from the debt/exports ratio and the volatility of external flows. The results do not change significantly,
and are indeed very similar to the ones using the original indicators.
3.3. Counterfactual analysis for Latin America – General Model
To assess the potential effect that the modified solvency ratio as well as the volatility indicator could have
on the potential rating for some countries on Latin America, we construct a simple counterfactual
scenario, looking at the rating evolution when remittances flows are taken into account. We use the
observed ratio debt-to-exports and the counterfactual ratio debt-to-exports that we estimated for the
previous regressions, this is, excluding remittances flows (TDX_wr). By the same, we use the observed
volatility indicator and the counterfactual volatility indicator that we estimated for the previous
regressions, this is, excluding remittances flows (volat_indicator_wr). We estimate our initial model with
the counterfactual variable:
Rating i ,t   0   1GDP _ pc   2GDP _ growthi ,t   3 Inflat i ,t   4 Fisc _ budg i ,t   5CAi ,t   6TDX _ wri ,t
  7 Default i ,t   8 Re servesi ,t   9Volat _ indicatori ,t   7 EMBI i ,t   t  i   i ,t
and we obtain the vector ˆ as the fixed-effect estimator. Then, we use the observed ratio debt-toexports (TDX) as well as the volatility of external flows and calculate the change in the observed rating
using this ratio and the ˆ coefficients. We obtain the potential improvement in the sovereign ratings for
the Latin American countries included in the sample. Figure 6a depicts the observed rating for Standard
and Poor’s (“Observed” Y in the figure), the predicted model (“Predicted” Yˆ in the figure) that estimates
ratings depending on the observed TDX_wr ratio (debt-to-exports excluding remittances) and on the
observed volat_indicator_wr ratio (volatility of external flows excluding remittances) as well as the
potential rating in the scenario including workers’ remittances in our variable of interests, debt/exports
~
and volatility of external flows (“Counterfactual” Y in the figure).
Figure 6a, 6b, and 6c also provide shadow and predicted ratings for Latin American countries (in the
figures, both categories are included under the name “predicted”). The naming “shadow ratings”
concerns unrated countries for a given year and a specific agency, while predicted ratings refer to
21
countries that are already assigned a rating (see Table 6 for the two series of periods covering shadow
and predicted ratings respectively for each country and agency).
Table 6. Shadow and Predicted Ratings by CRA and by Country
Argentina
Bolivia
Brazil
Chile
Colombia
Costa Rica
Dominican Rep.
Ecuador
El Salvador
Guatemala
Honduras
Mexico
Nicaragua
Panama
Paraguay
Peru
Uruguay
Venezuela
S&P
NA
1993:1997
1993
NA
NA
1995:1996
1995:1996
1993:1999
1993:1995
1993:2000
1993:2006
NA
2002:2006
1994:1996
NA
1993:1996
NA
NA
"Shadow ratings"
Moody's
NA
1993:1997
NA
1993:1998
NA
1995:1996
1995:1998
1993:1996
1993:2001
1993:1996
1993:1998
NA
NA
1994:1996
1995:1997
1993:1998
NA
NA
Fitch
1993:1997
1993:2003
1993
1993:1995
1993
1995:1997
1995:2002
1993:2001
1993:1995
1993:2005
1993:2006
1993:1995
2002:2006
1994:1997
1995:2006
1993:1998
NA
NA
S&P
1993:2006
1998:2006
1994:2005
1993:2006
1993:2006
1997:2006
1997:2006
2000:2006
1996:2006
2001:2006
NA
1993:2006
NA
1997:2006
1995:2006
1997:2006
2002:2006
2003:2006
"Predicted ratings"
Moody's
Fitch
1993:2006
1997:2006
1998:2006
2004:2006
1993:2005
1994:2005
1999:2006
1996:2006
1993:2006
1994:2006
1997:2006
1998:2006
1999:2006
2003:2006
1997:2006
2002:2006
2002:2006
1996:2006
1997:2006
2006
1999:2006
NA
1993:2006
1995:2006
2002:2006
NA
1997:2006
1998:2006
1998:2006
NA
1999:2006
1999:2006
2002:2006
2002:2006
2003:2006
2003:2006
Sources: Fitch (2008a), Moody’s (2008), S&P (2007).
Results are very similar from one agency to the other. The main findings are the following. First, ratings
yielded by the “predicted” and the “counterfactual” models are, on average, lower than observed ratings
for investment grade issuers (Chile, Mexico) and Peru. By contrast, predicted and counterfactual ratings
are higher for defaulting countries (Argentina, Dominican Republic, Ecuador). This may prove that the
model tends to attenuate the boom-bust debt cycle. Second, when they are compared for a given
country, predicted and counterfactual ratings are very close, except for smaller countries (i.e. El Salvador,
Honduras, and Nicaragua): the latter being higher than the former. This gap could suggest that ratings of
small economies are more likely to be enhanced by the inclusion of remittances9.
Source: Authors’ calculation.
23
Uruguay
8
6
Observed
Counterf.
0
14
Predicted
4
Observed
2
Counterf.
2007
0
2007
Predicted
2006
6
2006
8
2005
10
2005
10
Venezuela
2004
12
2004
12
2003
2
2003
4
2002
12
2002
Panama
2001
2007
2006
2005
2004
2003
2002
Counterf.
2001
Observed
2
2000
4
2000
14
1999
2007
2006
2005
2004
2003
2002
2001
2000
1999
Counterf.
1999
Counterf.
Observed
2
1998
Predicted
4
1998
14
1998
Counterf.
1997
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
Rating
12
1997
Predicted
1996
10
1997
Observed
1995
9.5
1996
Honduras
1996
0
1996
Counterf.
1995
Predicted
1995
Observed
1995
Ecuador
1994
1993
0
1994
1993
2007
2006
2005
2004
2003
2002
Predicted
1994
Rating
6
1993
Rating
2007
2006
2005
2004
2003
2002
2001
10
1994
1993
Observed
Rating
2007
2006
2005
2004
2003
2002
2001
2000
1999
6
2007
2006
2005
2004
2003
2002
2001
2001
2000
1999
Colombia
2001
2
1998
1997
12
2000
4
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
2
1999
Predicted
Rating
12
1998
14
4
1997
0
Counterf.
1996
Counterf.
Predicted
1995
Observed
Observed
1994
8
Bolivia
1993
10
Rating
Peru
8
2007
0
2006
Counterf.
2005
Predicted
2004
Observed
2003
8
2002
10
14
12
10
8
6
4
2
0
-2
-4
-6
-8
2001
Nicaragua
2000
0
2000
Counterf.
2000
Predicted
1999
Observed
10
9
8
7
6
5
4
3
2
1
0
1999
8
Counterf.
1999
Guatemala
Observed
2
1998
10
4
1998
0
1998
2
1998
12
1997
Counterf.
1996
14
1997
4
1997
Predicted
1997
Observed
1996
8
1994
1993
Rating
10
9
8
7
6
5
4
3
2
1
0
1996
8
1996
10
1996
Dominican Repubic
1995
10
1995
Counterf.
1995
Predicted
1995
Observed
1994
Chile
1994
1993
0
1994
1993
2007
Counterf.
1994
Rating
2006
2005
2004
2003
2002
2001
2000
Predicted
1993
6
Rating
2007
2006
2005
2004
2003
2002
2001
1999
1998
Observed
1993
6
Rating
2007
2006
2005
2004
2003
2002
2001
2000
1999
1997
6
1995
6
Rating
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1996
8
1994
6
Rating
2007
2006
2005
2004
2003
2002
2001
2000
2000
1999
1998
1997
1995
1994
1993
Rating
10
1993
2007
2006
2005
2004
2003
2
2002
4
2001
12
2000
2
1999
4
1999
12
1998
2
1998
4
1998
12
1997
2
1997
4
1997
1996
1995
1994
1993
Rating
18
16
14
12
10
8
6
4
2
0
1997
1996
1995
1994
1993
Rating
12
1996
1995
1994
1993
Rating
2
1996
1995
1994
1993
Rating
4
1996
1995
1994
1993
Rating
12
Argentina
Figure 6a. Counterfactual Analysis for Latin America – S&P
Brazil
10
8
6
Observed
0
Predicted
Counterf.
11.5
Costa Rica
10.5
11
Observed
Predicted
9
Counterf.
12
El Salvador
10
8
6
Predicted
0
12
Mexico
10
8
6
Predicted
0
Paraguay
10
8
6
Observed
0
Predicted
Counterf.
Source: Authors’ calculation.
4
2
0
0
12
12
10
Predicted
Counterf.
0
Venezuela
10
8
6
4
2
Observed
Predicted
0
Counterf.
2007
2
2006
4
2007
Counterf.
2005
Observed
2006
10
2004
12
2005
12
2003
Panama
2004
2007
2006
2005
2004
Counterf.
2003
Predicted
1
2
2002
4
Counterf.
6
2003
Predicted
2002
2
2001
Predicted
2002
Observed
2001
3
2000
Observed
2001
16
2000
Honduras
1999
2007
2006
2005
2004
2003
2002
2001
2000
14
1999
Ecuador
1998
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
12
1999
12
1997
Colombia
1998
7
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
12
2000
Observed
1993
Rating
Bolivia
1999
Uruguay
Predicted
1998
0
Observed
2
1997
6
4
1998
2
1997
4
1997
10
1996
14
1996
9.5
1997
8
1995
Predicted
1996
Counterf.
1996
Predicted
1995
Observed
1995
0
1995
Counterf.
1994
Observed
1994
10
1996
6
1993
12
1994
0
1995
4
1993
2007
2006
2005
2004
2003
2002
6
1994
6
Rating
8
1993
Rating
2007
2006
2005
2004
2003
2001
2000
4
1993
Predicted
Rating
5
Rating
2007
2006
2005
2004
2003
2002
2001
1999
1998
5
2007
2007
2006
2005
2004
2003
2002
2001
2000
1999
7
1994
8
Rating
2006
2005
2004
2003
2002
2002
2001
2000
1999
1997
8
1993
2007
2
2006
Predicted
2005
4
2004
14
2003
Peru
2001
0
6
2002
Counterf.
8
2000
4
2001
14
2000
9
1999
14
1998
Counterf.
1998
Predicted
1
1999
6
1996
2
Counterf.
2000
7
1997
Predicted
1998
Nicaragua
1997
Rating
9
1999
8
1995
Observed
1998
0
1996
3
1997
Counterf.
1994
1993
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
Moodys Rating
Observed
1998
8
1997
10
1996
Guatemala
1997
0
1996
Counterf.
10
9
8
7
6
5
4
3
2
1
0
1996
Predicted
1995
2
1995
Observed
1995
8
1995
10
1994
Dominican Repubic
1994
4
1996
Counterf.
1993
0
1994
12
1995
Observed
1993
Rating
14
1994
6
Rating
2007
2006
2005
2004
2003
2002
8
1993
6
Rating
2007
2006
2005
2004
2003
2002
2001
2000
10
1993
5
Rating
2007
2006
2005
2004
2003
2002
2001
2000
Chile
1994
Rating
2007
2006
2005
2004
2003
2002
2001
2000
1999
16
1993
2007
2006
2005
2004
2003
2002
Predicted
2001
2
2001
Observed
2000
3
2000
9
1999
Counterf.
1999
Predicted
2
1999
1998
4
1998
12
1998
1997
Observed
1998
1997
1996
1995
1994
1993
Rating
18
1999
1
1997
2
1997
4
1996
Rating
6
1998
10
9
8
7
6
5
4
3
2
1
0
1996
1995
1994
1993
2
1996
1995
1994
1993
Rating
4
1997
1995
1994
1993
Rating
12
1996
1995
1994
1993
Rating
10
9
8
7
6
5
4
3
2
1
0
Argentina
Figure 6b. Counterfactual Analysis for Latin America – Moody’s
Brazil
10
8
6
0
Counterf.
Costa Rica
11.5
10.5
11
Observed
9
Predicted
Counterf.
El Salvador
12
10
8
6
Observed
Predicted
0
Counterf.
Mexico
10
8
Observed
0
Paraguay
10
8
6
Observed
Predicted
Counterf.
Source: Authors’ calculation.
25
Uruguay
Observed
0
6
16
14
12
12
10
10
Venezuela
8
Predicted
4
Predicted
6
4
Observed
Predicted
Counterf.
2
Counterf.
2
0
Counterf.
2007
16
2007
Observed
Counterf.
2006
Panama
Observed
2
2005
0
4
2006
Counterf.
2004
Predicted
2005
Observed
2004
14
2003
Counterf.
2003
0
2002
2
2002
Predicted
Counterf.
2001
4
2
6
2001
Predicted
2006
2007
2007
2003
2002
2007
2003
2002
2007
2001
2000
1999
1998
1997
1996
1995
1994
1993
2007
2006
2005
2004
2003
2002
2005
0
2006
Predicted
2006
6
2006
8
2004
10
2005
Mexico
2005
12
2005
0
2004
Observed
2004
8
2004
10
2003
El Salvador
2003
0
2002
Observed
2002
8
2001
2001
2000
Rating
Costa Rica
2001
2000
Observed
2000
4
2000
Counterf.
1999
2
1999
Predicted
2
1998
4
Counterf.
1998
Predicted
2
1999
12
1997
4
Counterf.
1999
10
1996
Predicted
2000
8
Predicted
1998
8
Observed
2
1998
14
1997
16
1997
Ecuador
1995
4
6
1996
12
1995
0
1994
Observed
Observed
1996
10
1993
10
1994
12
10
1995
Honduras
1993
2007
2006
2005
2004
12
1994
6
Rating
8
1993
6
Rating
2007
2006
2005
2004
2003
2001
2000
1999
1998
1997
Rating
12
1999
Rating
2007
2006
2005
2004
2003
2002
16
1997
2007
2006
2005
2004
2003
2002
8
4
1998
Counterf.
2003
2002
2001
14
1996
2
2002
2001
2000
14
1995
Predicted
Counterf.
2001
2001
2000
Colombia
1994
1993
4
2000
2000
1999
1998
14
1997
14
Rating
Predicted
1999
1999
1998
1996
Counterf.
1996
6
1997
1995
1994
Predicted
1995
0
Observed
1994
Observed
Bolivia
1993
8
2007
10
2006
16
2005
Peru
2004
Counterf.
2003
Predicted
2002
Observed
16
14
12
10
8
6
4
2
0
-2
-4
2001
Nicaragua
2000
0
1999
2
1999
12
1998
4
1997
14
1998
Predicted
1996
16
1998
6
1997
10
1997
12
1997
0
1996
Predicted
1996
Counterf.
1995
6
6
1996
Guatemala
1993
Rating
10
9
8
7
6
5
4
3
2
1
0
1996
Observed
1995
8
1995
Dominican Repubic
1995
10
1994
0
1994
8
1993
10
1994
Chile
1993
2007
0
1994
12
16
Rating
2006
2005
2004
2003
2002
2001
2000
Predicted
1993
6
Rating
2007
2006
2005
2004
2003
2002
2001
6
1993
8
Rating
2007
2006
2005
2004
2003
2002
2001
1999
1998
8
1995
Rating
2007
2006
2005
2004
2003
2002
2001
2000
1999
10
1994
6
Rating
2007
2006
2005
2004
2003
2002
2001
2000
1999
1997
Rating
12
1993
2007
2006
2005
2004
2003
2002
12
2001
Counterf.
2000
2
2000
Observed
2000
4
1999
1998
18
1999
1998
1996
Counterf.
1998
1997
1995
1994
1993
2
1998
1997
1996
1995
1994
1993
Rating
Observed
1999
2
1997
14
1997
Rating
4
1998
4
1996
10
9
8
7
6
5
4
3
2
1
0
1996
1995
1994
1993
2
1996
1995
1994
1993
Rating
4
1997
1995
1994
1993
Rating
12
1996
1995
1994
1993
Rating
14
Argentina
Figure 6c. Counterfactual Analysis for Latin America – Fitch
Brazil
10
8
6
0
Counterf.
Paraguay
14
12
10
8
Observed
0
Including the debt-to-exports ratio and the volatility of external flows in the estimation does not
substantially alter the estimation. We infer that including remittances in the rating agencies’ model does
not have an effect on improving the rating for most Latin American countries. To check the robustness of
this result, we test the opposite estimation, using the variables TDX and Volat_indicator as follows:
Rating i ,t   0   1GDP _ pc   2GDP _ growthi ,t   3 Inflat i ,t   4 Fisc _ budg i ,t   5CAi ,t   6TDX i ,t
  7 Default i ,t   8 Re servesi ,t   9Volat _ indicatori ,t   7 EMBI i ,t   t  i   i ,t
and doing the counterfactual with the variable excluding remittances (TDX_wr). The results are very
similar to those presented in Figures 6a, 6b, and 6c10.
3.3. Model for High-Remittance Receptors
The wide range of countries in the sample makes it difficult to identify if there is a clear impact of
remittances flows in the regressions. Initially, we would expect that for those countries where
remittances have a non-negligible weight (as a share of GDP) in the economy, the change in our two
benchmark variables (i.e., solvency ratio and volatility indicator) including and excluding remittances
would be significant. For this reason, we decide to calculate a threshold variable (for each country and
year) taking the value 1 when the ratio Remittances/GDP was higher than a given percentage and zero
otherwise. The objective is to identify those countries and years where remittances are more important.
Note that this dummy is non constant over time, and therefore can be included in the fixed-effect panel.
Then, we calculate a crossed term with the non-constant dummy and the variables TDX and
Volat_indicator, that will detect the interaction effect between countries with a high share of remittances
and our variable of interest11.
Thus, we test the following model for the whole sample:
Rating i ,t   0   1GDP _ pc   2 GDP _ growthi ,t   3 Inflat i ,t   4 Fisc _ budg i ,t   5CAi ,t   6TDX i ,t   7 Default i ,t
  8 Re servesi ,t   9Volat _ indicatori ,t  10 EMBI i ,t  11Threshold  TDX  12Threshold  Volat _ indicatori ,t
 13Threshold   t   i   i ,t
where Threshold takes the value 1 when the ratio Remittances/GDP was higher than a given percentage
and zero otherwise. Threshold  TDX and Threshold  Volat _ indicator are the interaction effects
between countries with a high share of remittances and the ratio debt over exports and the volatility of
external flows respectively.
10
11
These results are not reported but they can be provided under request.
We tested other configurations to take into account the importance of isolating those ratings most likely to be affected by the
remittances flows. We included the ratio remittances-to-GDP as an explanatory variable in equation (1), but this was not
significant for the sample. Also, we separated the sample in income groups, following the World Bank classification (lower
income/middle income/higher income, etc.) and performed regressions on each group. Finally, we opted for the non-constant
dummy variable.
Table 7 summarizes the result for the sample, using two different thresholds: 3.5 and 5.0 per cent,
respectively.
`
Table 7. Regression with Threshold Model
Threshold: 3.5%
Threshold: 5.0%
S&P
ME
Moodys
ME
Fitch
ME
S&P
ME
Moodys
ME
Fitch
0.0010*** 4.1235 0.0011*** 4.5359 0.0008*** 3.2988
0.0010*** 4.1235 0.0011*** 4.5359 0.0008***
[9.472]
[11.22]
[6.283]
[8.931]
[10.72]
[6.057]
GDP growth (annual)
-0.0385*
-0.1460
-0.0390*
-0.1479
-0.0244
-0.0925
-0.0375
-0.1422
-0.0385*
-0.1460
-0.0137
[-1.652]
[-1.965]
[-1.012]
[-1.569]
[-1.913]
[-0.553]
Annual inflation
-0.0013** -0.0632
-0.0002
-0.0097
-0.0013*
-0.0632
-0.0013** -0.0632
-0.0002
-0.0097
-0.0014*
[-2.177]
[-0.595]
[-1.698]
[-2.139]
[-0.542]
[-1.677]
Fiscal Budget
-0.0362
0.0884 -0.1090*** 0.2663 -0.1640*** 0.4007
-0.0217
0.0530 -0.1022*** 0.2497 -0.1693***
[-0.996]
[-3.476]
[-3.125]
[-0.586]
[-3.223]
[-3.137]
Current Account (% GDP)
-0.1447*** 0.5128 -0.0879*** 0.3115 -0.0999*** 0.3541
-0.1526*** 0.5408 -0.0890*** 0.3154 -0.0912***
[-6.126]
[-4.309]
[-4.786]
[-6.274]
[-4.321]
[-4.320]
Default dummy (20 years)
-1.3282*** -0.9704
-0.5458
-0.3988
-0.2825
-0.2064
-1.0153** -0.7418
-0.4625
-0.3379
-0.3351
[-3.214]
[-1.562]
[-0.475]
[-2.435]
[-1.314]
[-0.549]
Reserves Ratio
-0.0043
-0.0662
0.0265*
0.4079
-0.0441
-0.6787
0.0101
0.1554
0.0323**
0.4971
-0.0347
[-0.261]
[1.860]
[-1.541]
[0.603]
[2.270]
[-1.191]
Volatility External Flows
-314.7492*** -1.5265 -197.1642*** -0.9562 -248.3374*** -1.2044
-344.3752*** -1.6701 -207.8495*** -1.0080 -225.2765***
[-5.589]
[-3.866]
[-4.413]
[-5.975]
[-4.023]
[-3.999]
EMBI dummy
0.9925*** 0.2033
0.4567
0.0936
0.9060**
0.1856
1.0711*** 0.2194
0.4375
0.0896
0.9737**
[3.130]
[1.630]
[2.012]
[3.357]
[1.547]
[2.092]
Threshold dummy x (Debt/exports)
-0.0023
-0.5067
0.0011
0.2423
-0.0059
-1.2999
-0.0027
-0.5948
0.0006
0.1322
-0.0125
[-0.480]
[0.411]
[-1.215]
[-0.457]
[0.208]
[-1.556]
Threshold dummy x (Volat. External flows)245.5258** 1.1907 193.0439** 0.9362
123.8197
0.6005
340.2644*** 1.6502 265.8059*** 1.2891
192.1485
[2.108]
[2.019]
[1.157]
[2.776]
[2.667]
[1.277]
Solvency Ratio (debt/exports)
-0.0137*** -3.0183 -0.0072*** -1.5863
-0.0032
-0.7050
-0.0141*** -3.1064 -0.0073*** -1.6083
-0.003
[-7.079]
[-4.181]
[-1.322]
[-7.108]
[-4.120]
[-1.147]
Threshold dummy
1.9353*** 0.6517
0.4962
0.1671 2.2812*** 0.7682
1.6900**
0.5691
-0.1591
-0.0536
0.9216
[2.686]
[1.041]
[2.656]
[2.022]
[-0.314]
[0.663]
Observations
320
306
242
320
306
242
Number of country_id
42
38
40
42
38
40
R-squared
0.587
0.57
0.504
0.568
0.559
0.475
t statistics in brackets
*** p<0.01, ** p<0.05, * p<0.1
Note: M.E. refers to the product between the sample mean and the coefficient for each variable. For interactive variables the M.E. is calculated only for countries with
GDP per capital (PPP)
threshold dummy equal to 1.
Source: Authors’ calculation.
27
ME
3.2988
-0.0519
-0.0681
0.4136
0.3232
-0.2448
-0.5341
-1.0925
0.1995
-2.7539
0.9319
-0.6609
0.3104
Regressions in Table 7 allow isolating the effect that remittances can have for those countries where they
are more important. With the 3.5 per cent threshold, the dummy variable is significant for two of the
agencies. The interactive term for the volatility of external flows, also, is positive and significant.
Increasing the threshold to 5 per cent does have an effect on the dummy variable and it does also affect
the interactive variable, with a positive and significant effect on the sovereign rating. This result suggests
that for countries where remittances are more important, high remittances have a direct effect on ratings
(the dummy variable remittances over GDP is significant) but above all, and more relevant, they could
have a premium in their ratings, meaning that there is an indirect effect of remittances on ratings (the
interactive variable for the volatility of external flows is significant). Indeed, there is an insight. The
indirect impact of remittances on ratings goes mainly through the volatility of external flows and not
through the solvency ratio as argued in previous research.
For countries where the ratio remittances-to-GDP is higher than 3.5 per cent the elasticity of the rating
with respect to the variable volatility of external flows is  9  12 . Since 12 is positive, the weight of the
variable volatility of external flows is reduced. We find that including an interaction term between the
ratio remittances/GDP and the volatility of flows variable denotes a more inelastic rating for those
countries where precisely remittances are more important. In other words, for countries in which the
ratio of remittances over GDP is high there is an indirect effect of remittances. Indeed, there is a premium
for these economies. More precisely, for countries in which remittances are relatively high, the negative
impact of the volatility of external flows on the rating can be attenuated. In any case, as it is depicted in
Figures 7a, 7b, and 7c, the effect is somehow limited. We estimate the predicted rating for Latin
American countries, for the model including the threshold for remittances/GDP. The predicted ratings
are presented in Figure 7a, 7b, and 7c. The main finding is that predicted ratings are higher than observed
ratings for all speculative grade countries (except Bolivia).
Source: Authors’ calculation.
29
Uruguay
12
6
0
12
10
6
4
2
Observed
0
Predicted
2007
8
2007
10
2006
Venezuela
2006
10
2005
12
10
9
8
7
6
5
4
3
2
1
0
2005
Panama
2004
2005
2006
2007
2005
2006
2007
2000
2004
0
2004
Predicted
2004
Observed
2
2003
6
2003
8
2002
10
2002
4
Predicted
2003
0
Mexico
2001
12
2001
Observed
2003
2
2002
4
2002
6
2001
14
2000
7
Observed
2000
0
1999
2
1999
Predicted
1999
4
2
1998
Observed
1998
14
1998
4
1997
8
1997
10
Predicted
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
2007
2006
2005
2004
2003
2002
2001
2000
1999
2007
Observed
2006
6
2007
Costa Rica
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
0
1997
Ecuador
1995
2
1996
0
1996
Predicted
1996
2
1994
4
Predicted
1995
Observed
1995
1993
4
1994
Observed
1995
Honduras
1993
8
Rating
10
8
1994
8
1993
6
Rating
2007
10
1994
5
Rating
2007
2006
2005
2004
2003
2002
6
1993
Rating
2007
2006
2005
2004
2003
2002
12
2001
8
Rating
2007
2006
2005
2004
2003
2002
2001
14
2000
Predicted
Rating
12
1999
2
2001
Colombia
1998
Predicted
2001
12
1997
2
2000
2
1996
Observed
2000
1998
1997
4
Predicted
1995
4
2000
1999
1998
1996
Observed
Observed
1994
Observed
1999
1998
1997
1995
1994
Bolivia
1993
4
2007
14
2006
0
2005
0
2006
2
2004
1
2005
0
Predicted
2003
4
2002
2
2004
8
Observed
6
2003
10
8
2002
5
2001
6
2001
14
2000
Nicaragua
2000
7.5
1999
1
1998
3
1999
9
1998
Predicted
1997
12
1999
Observed
1997
10
1997
9.5
1996
Guatemala
1996
0
1996
14
1998
Peru
1993
Rating
10
9
8
7
6
5
4
3
2
1
0
1997
6
1996
10
1995
Dominican Repubic
1995
12
1996
0
1995
4
1995
8
1994
12
1995
14
1994
Chile
1993
16
1994
2007
0
1993
10
Rating
2006
2005
2004
2003
2002
6
1993
8
Rating
2007
2006
2005
2004
2003
2001
2000
8
1994
9
Rating
2007
2006
2005
2004
2003
2002
2001
10
1993
Rating
2007
2006
2005
2004
2003
2002
2001
1999
Argentina
1994
6
Rating
2007
2006
2005
2004
2003
2002
2001
2000
1998
Rating
12
1993
2007
2006
2005
2004
2003
12
2002
Predicted
2002
3
2001
Observed
2001
4
2000
Predicted
2000
2
2000
Observed
2000
4
1999
18
1999
1997
Predicted
1999
1998
2
1999
1998
1996
Observed
1999
7
1998
1997
1995
1994
1993
4
1998
1997
1996
1995
1994
1993
Rating
14
1998
10.5
1997
1996
1995
1994
1993
Rating
2
1997
Rating
6
1997
8
1996
1995
1994
1993
8.5
1996
1995
1994
1993
Rating
14
1996
1995
1994
1993
Rating
Figure 7a. Predicted vs. observed analysis for Latin America – S&P All
Brazil
10
8
6
0
Predicted
12
El Salvador
10
8
6
Predicted
0
Paraguay
Observed
Predicted
Source: Authors’ calculation.
9.5
Uruguay
14
12
10
10
8
6
8
0
Observed
4
Observed
6
4
Observed
Predicted
2
Predicted
2
0
Predicted
2007
Venezuela
2007
1
2006
Predicted
2006
3
2005
Observed
2005
10
2004
10.5
2004
6
2003
7
2003
12
11.5
2002
6
2002
9
2001
Panama
2001
0
2006
2007
2006
2007
2007
2002
2001
2000
1999
1998
1997
2006
Predicted
2005
0
2005
Observed
2005
6
2004
8
2004
Mexico
2004
0
2003
Predicted
2003
6
2003
8
2002
10
2002
El Salvador
2001
2000
1999
12
2001
2000
2
2000
4
Predicted
2000
16
1999
Observed
1998
14
1999
3
1997
Ecuador
1999
10
1996
Colombia
1998
12
5
1995
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
12
1998
6
1994
Rating
Bolivia
1998
14
1997
Honduras
1997
0
1997
2
1996
4
Predicted
1996
Observed
Observed
1996
4
12.2
12
11.8
11.6
11.4
11.2
11
10.8
10.6
10.4
10.2
10
1996
0
1994
10
1995
12
1995
0
1995
1993
2
1995
1993
Observed
4
Predicted
1994
6
1993
2007
2
Observed
1994
8
Rating
2006
2005
2004
4
1993
6
Rating
2007
2006
2005
2003
2002
2001
2000
6
1994
7
Rating
2007
2006
2005
2004
2003
2002
2001
1999
5
1993
11
Rating
2007
2006
2005
2004
2003
2002
2001
2000
7
1994
12
Rating
2007
2006
2005
2004
2003
2002
2001
2000
1998
1997
1996
8
1993
2007
14
2006
Peru
2005
0
2004
1
2004
2
2003
Predicted
2003
3
2002
7
2002
12.5
2001
0
2001
1
2000
2
2000
Predicted
2000
Observed
1999
Predicted
1999
Observed
2
1999
4
Predicted
1999
8
1998
Observed
1999
Guatemala
1998
12
1998
8
1997
14
1998
10
1995
Rating
9
1998
2
1997
4
1997
0
1996
1
1997
Predicted
1994
1993
3
1997
Observed
1996
8
1996
10
8
1996
10
1996
Dominican Repubic
1995
0
1995
4
1995
12
1995
14
1994
Chile
1995
Nicaragua
1993
16
1994
8
1993
2007
0
1994
10
Rating
2006
2005
2004
2003
2002
2001
2000
6
1993
6
Rating
2007
2006
2005
2004
2003
2002
2001
1999
10
1994
6
Rating
2007
2006
2005
2004
2003
2002
2001
2000
1998
8
1993
Observed
Rating
2007
2006
2005
2004
2003
2002
2001
2000
1999
12
1994
Rating
2007
2006
2005
2004
2003
2002
2001
2000
1999
1997
Rating
Argentina
1993
2007
2006
2005
2004
2003
2002
2001
2000
4
1999
18
2000
8
1998
Predicted
1999
5
1998
2
1999
12
1998
1996
1995
1994
1993
Observed
1998
1997
Rating
4
1998
10
9
8
7
6
5
4
3
2
1
0
1997
2
1997
2
1996
1995
1994
1993
6
1997
Rating
14
1997
4
1996
1995
1994
1993
2
1996
1995
1994
1993
Rating
4
1996
1995
1994
1993
Rating
12
1996
1995
1994
1993
Rating
Figure 7b. Counterfactual Analysis for Latin America – Moody’s All
10
Brazil
8
6
0
Predicted
Costa Rica
Observed
Predicted
8
Paraguay
5
4
2
Observed
0
Predicted
Source: Authors’ calculation.
31
2
3
1
0
0
0
12
Uruguay
12
10
6
0
4
Observed
2
Predicted
2007
1
2006
4
2007
2
2005
10
2006
12
2004
9
2005
Panama
2003
2007
2006
2005
2004
Predicted
2004
0
2003
Observed
2
2002
4
Predicted
2003
Observed
2002
14
2001
Honduras
2002
2007
2006
2005
2004
2003
2002
2
2001
0
2001
Predicted
2000
2
2001
Predicted
2000
4
2
2000
Observed
1999
4
1999
Observed
1999
14
1998
Ecuador
1998
Observed
4
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
12
1998
8
1996
Colombia
1997
6
1997
8
1997
10
8
1996
12
10
1996
9
1996
0
1995
9.5
1995
10
Predicted
1994
Observed
2
1995
4
Predicted
1995
6
1994
1993
Observed
1994
1993
8
Rating
10
1994
12
10
Rating
2007
2006
2005
2004
12
1993
6
Rating
2007
2006
2005
2004
2003
2002
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
2007
2006
2005
2004
2003
2002
2001
0
1993
Rating
2007
2006
2005
2004
2003
2002
2
2000
8
Rating
2007
2006
2005
2004
2003
2003
2002
2000
Rating
14
1999
Predicted
2001
Bolivia
1998
2
2001
4
Predicted
1997
Predicted
2001
1999
1998
Observed
1996
Observed
2000
1999
1997
6
1995
4
2000
1999
1998
1996
8
1994
0
1997
10
1993
Observed
2007
8
2006
10
2005
14
2004
Peru
6
Predicted
2002
3
Observed
2003
Predicted
8
2001
6
2002
7
2000
14
2001
Nicaragua
2000
0
1999
2
2000
12
1999
Guatemala
1998
Predicted
1998
14
1999
4
1997
14
1998
Observed
1997
8
1995
Rating
12
1998
10
8
1996
2
1997
10
1996
0
1994
4
1997
Dominican Repubic
1996
0
1996
12
1995
14
1996
Chile
1995
16
1995
0
1995
Predicted
1994
1993
4
1994
Observed
1995
6
1993
2007
12
1994
8
1993
10
Rating
2006
2005
2004
2003
2002
2001
2000
14
1994
12
Rating
2007
2006
2005
2004
2003
2002
2001
1999
1998
8
1993
6
Rating
2007
2006
2005
2004
2003
2002
2001
2000
1999
1997
10
1993
Observed
Rating
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
Argentina
1994
6
Rating
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1996
1995
1994
1993
Rating
16
1993
2007
2006
2005
2004
2003
2
2002
4
2001
12
2000
4
2000
5
1999
8
1999
12
1998
18
1998
Rating
18
1998
2
1997
4
1997
1996
1995
1994
1993
6
1997
1996
1995
1994
1993
Rating
2
1997
Rating
6
1997
2
1996
1995
1994
1993
4
1996
1995
1994
1993
Rating
14
1996
1995
1994
1993
Rating
Figure 7c. Predicted vs observed analysis for Latin America – Fitch All
Brazil
12
10
8
6
Observed
0
Predicted
Costa Rica
11.5
10.5
11
Observed
Predicted
El Salvador
6
0
Predicted
Mexico
12
10
8
6
0
8
Paraguay
7
6
5
4
2
Observed
Predicted
10
Venezuela
8
6
0
4. Conclusion
This paper analyzes the impact of remittance flows on sovereign ratings for developing and emerging
countries over the period 1993-2006. Remittances are an important source of external financing for
developing countries. They may also have served to reducing country default risk most significantly in the
smaller economies. In order to capture the impact of remittances on sovereign risk, we analyzed two core
variables. First, we used a traditional solvency ratio employed in the literature and second we introduce a
second determinant, the volatility of external flows.
Using a model of the determinants of sovereign ratings and then a counterfactual estimation, we find
that the impact of including remittances in both the ratio debt-to-exports and the volatility of external
flows is weak. This suggests that CRAs are either not sensitive remittance flows, or that the inclusion of
remittances in these two variables does not improve ratings.
When specifying a second model for identifying highly remittance-dependent economies, we find that,
for this set of countries, remittances have an indirect, yet positive impact on ratings through a premium
(captured with the interactive dummy variable remittances over GDP and the volatility of external flows).
The results presented above are consistent with previous research (e.g. Roubini and Manasse, 2005)
showing that there is not a single model to rate countries and that not all variables have the same impact
on a sovereign rating. For the remittances variable, the countries most affected are those where
remittances account for a higher share of GDP. Indeed, the impact depends more on the volatility of
external flows than on the solvency ratio (debt over exports).
This paper also provides shadow ratings for countries which are not rated by the three main CRAs, in
particular some Central American and Caribbean countries, where relative remittances flows are higher.
Our analysis provides useful information on the potential ratings for some of these countries, a crucial
step for accessing international capital markets.
This approach implies that remittance flows cannot, as a whole, enable low and medium income
countries to improve their creditworthiness significantly. However, remittances may prove very useful for
some small Central American and Caribbean countries. Rating agencies, and other financial institutions,
should take remittances into consideration to rate countries with idiosyncratic profiles.
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